Recommending Database Architectures for Social Queries: A Twitter Case Study

Part 16: Recommendation SystemsInternational audienceDatabase deployment is a complex task depending on a multitude of operational parameters such as anticipated data scaling trends, expected type and volume of queries, uptime requirements, replication policies, available budget, and personnel training and experience. Thus, enterprise database administrators eventually rely on various performance metrics in conjunction to existing company policies in order to determine the best possible solution under these constraints. The recent advent of NoSQL databases, including graph databases such as Neo4j and document stores like MongoDB, added another degree of freedom in database selection since for a number of years relational databases such as PostgreSQL were the only available technology. In this work the scaling characteristics of a representative set of social queries executed on virtual machine installations of PostgreSQL and MongoDB are evaluated on a large volume of political tweets regarding Brexit. Moreover, Wiener filters for predicting the execution time of social query windows of fixed length over both databases are designed